Users are interested in reducing the time involved in finding media content of interest to them. To find content, users typically resort to content recommendation engines, which are able to predict content that a user might like based on tracked viewing habits of the user, the way the user has rated similar content, or based on demographic information.
However, it is often the case that users are dissatisfied with individual recommendation engines in the sense that those systems do not “understand” them. These engines are typically managed in isolation in that they are forced to work independently to gather pieces of information about a user. As such, each engine tends to have only a portion of the knowledge on an individual. This inherently limits the degree to which the engine is able to understand its audience and reduces the effectiveness of any resulting analytics on that data.